Information processing system, program, and information processing method

ABSTRACT

An information processing system includes a plurality of vehicle; and a server that is able to communicate with the plurality of vehicles. Each of the plurality of vehicles performs: generating a moving image in which a person outside the vehicle appears; and transmitting the moving image and an imaging time and an imaging position of the moving image to the server. The server performs: specifying two or more target moving images in which the same person appears out of a plurality of moving images received from the plurality of vehicles; detecting behavior of the person from the two or more target moving images; estimating attribute information of the person based on the detected behavior; and transmitting the estimated attribute information of the person and the imaging time and the imaging position of at least one of the target moving images to a client.

INCORPORATION BY REFERENCE

This application is a continuation of U.S. application Ser. No.17/500,046, filed Oct. 13, 2021 (allowed), which is a continuation ofU.S. application Ser. No. 16/687,797, filed Nov. 19, 2019 (now U.S. Pat.No. 11,170,207, issued on Nov. 9, 2021), which claims priority toJapanese Patent Application No. 2018-234145 filed on Dec. 14, 2018. Theentire disclosures of the prior applications are considered part of thedisclosure of the accompanying continuation application, and are herebyincorporated by reference.

BACKGROUND 1. Technical Field

The disclosure relates to an information processing system, a program,and an information processing method.

2. Description of Related Art

In the related art, a technique of detecting information on a personfrom an image of an onboard camera is known. For example, JapanesePatent Application Publication No. 2017-211888 (JP 2017-211888 A)discloses an image information authentication system that receivescomparison data such as digital photograph data of a person with a riskof wandering due to dementia from an interested party, registers thereceived comparison data, captures a scene outside a vehicle with anonboard camera, compares image information acquired from the onboardcamera as authentication data with the comparison data, and notifies theinterested party of a result of comparison.

SUMMARY

However, in the related art, since comparison data for a person who isdetected from a moving image of the onboard camera needs to beregistered in advance, a person who has not been registered ascomparison data cannot be detected. In this way, convenience of thetechnique according to the related art cannot be said to be high.Accordingly, there is room for improvement in convenience of thetechnique of detecting information on a person from an image of anonboard device.

The disclosure is for improving convenience of a technique of detectinginformation on a person from an image of an onboard camera.

According to an embodiment of the disclosure, there is provided aninformation processing system including: a plurality of vehicles; and aserver that is able to communicate with the plurality of vehicles. Eachof the plurality of vehicles performs: generating a moving image inwhich a person outside the vehicle appears; and transmitting the movingimage and an imaging time and an imaging position of the moving image tothe server. The server performs: specifying two or more target movingimages in which the same person appears out of a plurality of movingimages received from the plurality of vehicles; detecting behavior ofthe person from the two or more target moving images; estimatingattribute information of the person based on the detected behavior; andtransmitting the estimated attribute information of the person and theimaging time and the imaging position of at least one of the targetmoving images to a client.

According to an embodiment of the disclosure, there is provided aprogram causing an information processing device, which is able tocommunicate with a plurality of vehicles, to perform: receiving aplurality of moving images, which is generated by the plurality ofvehicles and in which a person outside the vehicle appears, and imagingtimes and imaging positions of the plurality of moving images;specifying two or more target moving images in which the same personappears out of the plurality of moving images; detecting behavior of theperson from the two or more target moving images; estimating attributeinformation of the person based on the detected behavior; andtransmitting the estimated attribute information of the person and theimaging time and the imaging position of at least one of the targetmoving images to a client.

According to an embodiment of the disclosure, there is provided aninformation processing method which is performed by an informationprocessing system including a plurality of vehicles and a server that isable to communicate with the plurality of vehicles, the informationprocessing method including: causing each of the plurality of vehiclesto generate a moving image in which a person outside the vehicleappears; causing each of the plurality of vehicles to transmit themoving image and an imaging time and an imaging position of the movingimage to the server; causing the server to specify two or more targetmoving images in which the same person appears out of a plurality ofmoving images received from the plurality of vehicles; causing theserver to detect behavior of the person from the two or more targetmoving images; causing the server to estimate attribute information ofthe person based on the detected behavior; and causing the server totransmit the estimated attribute information of the person and theimaging time and the imaging position of at least one of the targetmoving images to a client.

With the information processing system, the program, and the informationprocessing method according to the embodiment of the disclosure, it ispossible to improve convenience of a technique of detecting informationon a person from an image of an onboard camera.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, advantages, and technical and industrial significance ofexemplary embodiments of the disclosure will be described below withreference to the accompanying drawings, in which like numerals denotelike elements, and wherein:

FIG. 1 is a diagram schematically illustrating a configuration of aninformation processing system according to an embodiment of thedisclosure;

FIG. 2 is a block diagram schematically illustrating a configuration ofa vehicle;

FIG. 3 is a block diagram schematically illustrating a configuration ofa server;

FIG. 4 is a diagram illustrating an example of information which isstored in the server;

FIG. 5 is a flowchart illustrating an operation of the vehicle; and

FIG. 6 is a flowchart illustrating an operation of the server.

DETAILED DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment of the disclosure will be described.

(Configuration of Information Processing System)

An outline of an information processing system 1 according to anembodiment of the disclosure will be described below with reference toFIG. 1 . The information processing system 1 includes a plurality ofvehicles 10 and a server 20. Each vehicle 10 is, for example, anautomobile, but is not limited thereto and may be an arbitrary vehicle.In FIG. 1 , for the purpose of convenience of explanation, two vehicles10 are illustrated, but the information processing system 1 may includean arbitrary number of vehicles 10. The server 20 includes oneinformation processing device (for example, a server device) or aplurality of information processing devices that can communicate witheach other. The vehicle 10 and the server 20 can communicate with eachother, for example, via a network 30 including a mobile communicationnetwork and the Internet. The server 20 can communicate with a client 40via the network 30. The client 40 is, for example, a personal computer(PC), a server device, or a smartphone, but may be an arbitraryinformation processing device.

The outline of this embodiment will be first described below and detailsthereof will be described later. The information processing system 1 isused, for example, to detect a wandering person or a suspicious person.Each of the plurality of vehicles 10 includes, for example, an onboardcamera and generates a moving image by capturing a scene outside thevehicle. For example, a person such as a pedestrian who is near thevehicle 10 may appear in the moving image. When a moving image isgenerated by imaging a person outside the vehicle, each vehicle 10transmits the moving image, and an imaging time and an imaging positionof the moving image to the server 20.

The server 20 specifies two or more moving images in which the sameperson appears out of a plurality of moving images which is generated bythe plurality of vehicles 10 as target moving images. Hereinafter, thesame person appearing in the target moving images is also referred to asa target person. Here, it is known that a person such as a wanderingperson or a suspicious person exhibits characteristic behavior differentfrom those of other persons. Accordingly, whether a wandering person ora suspicious person is applicable can be determined based on behavior ofa person. The server 20 detects behavior of a target person from the twoor more target moving images and estimates attribute information of thetarget person (for example, whether the target person corresponds to awandering person or a suspicious person) based on the detected behavior.Then, the server 20 transmits the estimated attribute information of thetarget person and imaging times and imaging positions of the targetmoving images to the client 40.

In general, in order to detect behavior of a person from a moving imageof the person, a series of motions of the person needs to appear in themoving image. However, for example, when each vehicle 10 images a personwhile traveling, the length of each moving image is relatively short andonly a part of a series of motions of a person appears in one targetmoving image. Accordingly, detection accuracy for behavior of a personfrom one target moving image may not be satisfactory or behavior may notbe detected from the target moving image. On the other hand, accordingto this embodiment, two or more target moving images in which the sameperson appears are specified out of a plurality of moving images whichis generated by a plurality of vehicles 10 as described above. Even whenonly a part of a series of motions of a person appears in each targetmoving image as described above, a series of motions of a person can bedetected from two or more target moving images. That is, detectionaccuracy for behavior of a target person is improved using two or moretarget moving images. Improvement in detection accuracy for behavior ofa target person causes improvement in estimation accuracy for attributeinformation of the target person. Accordingly, it is possible to improveconvenience of a technique of detecting information on a person from animage of the onboard camera.

Elements of the information processing system 1 will be described belowin detail.

(Configuration of Vehicle)

As illustrated in FIG. 2 , each vehicle 10 includes a communication unit11, a positioning unit 12, an imaging unit 13, a storage unit 14, and acontrol unit 15. The communication unit 11, the positioning unit 12, theimaging unit 13, the storage unit 14, and the control unit 15 may beincorporated into the vehicle 10 or may be detachably provided in thevehicle 10. The communication unit 11, the positioning unit 12, theimaging unit 13, the storage unit 14, and the control unit 15 arecommunicatively connected to each other, for example, via an onboardnetwork such as a controller area network (CAN) or a dedicated line.

The communication unit 11 includes a communication module that isconnected to the network 30. The communication module corresponds to amobile communication standard such as 4th generation (4G) and 5thgeneration (5G), but is not limited thereto and may correspond to anarbitrary communication standard. For example, an onboard communicationdevice such as a data communication module (DCM) may serve as thecommunication unit 11. In this embodiment, the vehicle 10 is connectedto the network 30 via the communication unit 11.

The positioning unit 12 includes a receiver corresponding to a satellitepositioning system. The receiver corresponds to, for example, a globalpositioning system (GPS), but is not limited thereto and may correspondto an arbitrary satellite positioning system. For example, a carnavigation device may serve as the positioning unit 12. In thisembodiment, the vehicle 10 acquires position information of the hostvehicle using the positioning unit 12.

The imaging unit 13 includes an onboard camera that generates a movingimage by imaging a subject in a view. The onboard camera may be amonocular camera or a stereoscopic camera. The imaging unit 13 isprovided in the vehicle 10 such that a scene outside the vehicle can becaptured. For example, an electronic device having a camera functionsuch as a drive recorder or a smartphone which is used by an occupantmay serve as the imaging unit 13. In this embodiment, the vehicle 10generates a moving image in which a scene outside the vehicle iscaptured using the imaging unit 13.

The storage unit 14 includes one or more memories. In this embodiment,each memory may be, for example, a semiconductor memory, a magneticmemory, or an optical memory, but is not limited thereto. Each memoryincluded in the storage unit 14 may serve as, for example, a mainstorage device, an auxiliary storage device, or a cache storage device.The storage unit 14 stores arbitrary information which is used foroperation of the vehicle 10. For example, the storage unit 14 may storea system program, an application program, and embedded software.Information stored in the storage unit 14 may be updated, for example,based on information which is acquired from the network 30 via thecommunication unit 11.

The control unit 15 includes one or more processors. In this embodiment,a “processor” is a general-purpose processor or a dedicated processorspecialized in a specific process, but is not limited thereto. Anelectronic control unit (ECU) which is mounted in the vehicle 10 mayserve as the control unit 15. The control unit 15 has a clockingfunction of acquiring a current time. The control unit 15 controls thewhole operation of the vehicle 10.

For example, the control unit 15 generates a moving image by capturing ascene outside the vehicle using the imaging unit 13. As described above,for example, a person such as a pedestrian who is near the vehicle 10may appear in the moving image. When a scene in which a person outsidethe vehicle appears is detected by image recognition while capturing amoving image, the control unit 15 may generate a moving image in whichthe person outside the vehicle appears by cutting out the scene. Anarbitrary image recognition technique such as pattern matching, featurepoint extraction, or machine learning can be employed to detect a scenein which a person outside the vehicle appears. When a moving image inwhich a person outside the vehicle is captured is generated, the controlunit 15 transmits the moving image and an imaging time and an imagingposition of the moving image to the server 20 via the communication unit11. The imaging time is, for example, a time at which capturing of themoving image has been started, but may be an arbitrary time in a periodfrom a time point at which capturing of the moving image has beenstarted to a time point at which the capturing has been ended. Theimaging position is a position of the vehicle 10 at the imaging time andis acquired from the positioning unit 12.

(Configuration of Server)

As illustrated in FIG. 3 , the server 20 includes a server communicationunit 21, a server storage unit 22, and a server control unit 23.

The server communication unit 21 includes a communication module that isconnected to the network 30. For example, the communication modulecorresponds to a wired local area network (LAN) standard, but is notlimited thereto and may correspond to an arbitrary communicationstandard. In this embodiment, the server 20 is connected to the network30 via the server communication unit 21.

The server storage unit 22 includes one or more memories. Each memoryincluded in the server storage unit 22 may serve as, for example, a mainstorage device, an auxiliary storage device, or a cache storage device.The server storage unit 22 stores arbitrary information which is usedfor operation of the server 20. For example, the server storage unit 22may store a system program, an application program, and a database.Information which is stored in the server storage unit 22 may be updatedwith, for example, information which is acquired from the network 30 viathe server communication unit 21.

The server control unit 23 includes one or more processors. The servercontrol unit 23 controls the whole operation of the server 20.

For example, when a moving image, an imaging time, and an imagingposition are received from a vehicle 10, the server control unit 23stores the received information in a database of the server storage unit22. For example, as illustrated in FIG. 4 , the server control unit 23may store a moving image, an imaging time, and an imaging position inthe database in correlation with a moving image ID. The moving image IDis information for allowing the server control unit 23 to uniquelyidentify a moving image and is automatically generated, for example,when a moving image is received. In the example illustrated in FIG. 4 ,two moving images including a moving image captured at an imaging timet1 and an imaging position p1 and a moving image captured at an imagingtime t2 and an imaging position p2 are stored. However, a plurality ofmoving images which is received from a plurality of vehicles 10 may bestored in the database.

The server control unit 23 selects two or more moving images in whichthe same person appears out of the plurality of moving images stored inthe database of the server storage unit 22, and specifies the selectedmoving images as target moving images. Specifically, the server controlunit 23 detects persons who appear in the moving images stored in thedatabase by image recognition and selects two or more moving images inwhich the same person appears by determining identity of the detectedpersons. For example, an arbitrary image recognition technique such aspattern matching, feature point extraction, or machine learning may beemployed to detect persons who appear in the moving images and todetermine identify thereof.

At the time of selection of the moving images, the server control unit23 may select two or more moving images in which the same person appearsout of a plurality of moving images of which imaging times and imagingpositions match in a predetermined range among the plurality of movingimages stored in the database. Specifically, the server control unit 23selects two or more moving images in which the same person appears byspecifying a plurality of moving images of which imaging times andimaging positions match in a predetermined range based on the imagingtimes and the imaging positions of the moving images stored in thedatabase, detecting persons who appear in the specified moving images byimage recognition, and determining identity of the detected persons.Here, the “plurality of moving images of which imaging times and imagingpositions match in a predetermined range” is a plurality of movingimages in which a difference in imaging time is equal to or less than areference value (for example, 1 minute) and a difference in imagingposition is equal to or less than a reference value (for example, 60 m)in relationships between the moving images and at least one other movingimage.

According to this configuration, all the moving images stored in thedatabase do not need to be subjected to detection of a person anddetermination of identity and thus a process load is decreased. The“predetermined range” may be determined, for example, based onexperimental results or experimental rules. For example, as the“predetermined range” becomes narrower, an effect of decrease in processload becomes larger and it is more difficult to specify two or moretarget moving images. On the other hand, as the “predetermined range”becomes broader, it is easier to specify two or more target movingimages and an effect of decrease in process load becomes smaller.

The server control unit 23 detects behavior of a target person from thespecified two or more target moving images. A series of motions of atarget person in terms of an arbitrary viewpoint can be detected asbehavior. For example, a series of motions of parts of a body such as asight line, an expression, change of a face direction, change of a bodydirection, or motions of hands and legs of a target person orcombinations thereof may be detected as the behavior. For example, aseries of composite motions of a body such as running, stopping,bending, or a combination thereof may be detected as the behavior. Forexample, an arbitrary image recognition technique such as patternmatching, feature point extraction, or machine learning can be employedto detect behavior of a person.

The server control unit 23 estimates attribute information of the targetperson based on the detected behavior. In this embodiment, attributeinformation includes information indicating a result of determination ofwhether a person is a wandering person or a suspicious person. Theinformation indicating what a person is may be, for example, informationindicating whether a target person corresponds to a wandering person ora suspicious person or information indicating a likelihood that a targetperson will correspond to a wandering person or a suspicious person.

An arbitrary technique can be employed to estimate attribute informationbased on behavior. As described above, it is known that a person such asa wandering person or a suspicious person exhibits characteristicbehavior which is different from those of other persons, and whether aperson is a wandering person or a suspicious person can be determinedbased on behavior of the person.

For example, a technique using correspondence information indicating acorrespondence relationship between human behavior and attributeinformation is employed. The correspondence relationship may bedetermined, for example, based on experimental results or experimentalrules. Specifically, the server control unit 23 stores thecorrespondence information indicating a correspondence relationshipbetween human behavior and attribute information in the server storageunit 22 in advance. The server control unit 23 estimates attributeinformation corresponding to the behavior detected from two or moretarget moving images as attribute information of the target person withreference to the correspondence information.

For example, a technique using a result of machine learning of acorrespondence relationship between human behavior and attributeinformation can be employed. Specifically, the server control unit 23stores a learned model with behavior of a person as input data and withattribute information of the person as output data in the server storageunit 22 in advance. The server control unit 23 inputs the behaviordetected from two or more target moving images to the model andestimates output attribute information as attribute information of thetarget person.

The server control unit 23 acquires information of a spot correspondingto an imaging position of at least one target moving image. Informationof a spot corresponding to an imaging position includes, for example, aname, a location, or an image of a facility which is near the spotcorresponding to the imaging position, but is not limited thereto andmay include arbitrary information on the spot. The information of a spotcorresponding to an imaging position may be acquired, for example, fromthe network 30 via the server communication unit 21 or may be acquiredfrom map information which is stored in the server storage unit 22 inadvance.

The server control unit 23 cuts out an image of a target person (amoving image or a still image) from one target moving image. Here, theserver control unit 23 may preferentially cut out an image of a scene inwhich a target person appears out of two or more target moving images.

The server control unit 23 transmits attribute information of a targetperson, an imaging time and an imaging position of at least one targetmoving image, information of a spot corresponding to the imagingposition, and an image of the target person to the client 40.Transmission of attribute information of a target person or the like maybe performed, for example, in response to a request from the client 40(for example, pull-transmission) or may be performed automatically bythe server control unit 23 (for example, push-transmission).

Here, operations including acquisition of information of the spotcorresponding to the imaging position, cutting-out of an image of atarget person, and transmission of attribute information of the targetperson may be performed only when the estimated attribute informationsatisfies a predetermined condition. For example, only when theestimated attribute information indicates that a target personcorresponds to a wandering person or a suspicious person or that alikelihood thereof is higher than a predetermined criterion, theoperations may be performed.

(Operation Flow of Vehicle)

A flow of operations of the vehicle 10 will be described below withreference to FIG. 5 .

Step S100: The control unit 15 generates a moving image in which aperson outside the vehicle is captured using the imaging unit 13.

Step S101: The control unit 15 transmits the moving image generated inStep S100 and an imaging time and an imaging position of the movingimage to the server via the communication unit 11.

(Flow of Operations in Server)

A flow of operations in the server 20 will be described below withreference to FIG. 6 .

Step S200: The server control unit 23 stores correspondence informationindicating a correspondence relationship between human behavior andattribute information in the server storage unit 22.

Step S201: The server control unit 23 receives moving images, imagingtimes, and imaging positions from the vehicles 10 and stores thereceived data in the database of the server storage unit 22.

Step S202: The server control unit 23 selects two or more moving imagesin which the same person appears out of a plurality of moving imagesstored in the database of the server storage unit 22 and specifies theselected moving images as target moving images. At the time of selectionof the moving images, the server control unit 23 may select two or moremoving images in which the same person appears out of a plurality ofmoving images in which the imaging times and the imaging positions matchin a predetermined range among the plurality of moving images stored inthe database.

Step S203: The server control unit 23 detects behavior of a targetperson from the specified two or more target moving images.

Step S204: The server control unit 23 estimates attribute information ofthe target person based on the detected behavior. In this example, theserver control unit 23 estimates attribute information corresponding tothe behavior detected from the two or more target moving images asattribute information of the target person with reference to thecorrespondence information in Step S200.

Step S205: The server control unit 23 acquires Information of a spotcorresponding to the imaging position of at least one target movingimage.

Step S206: The server control unit 23 cuts out an image of the targetperson (a moving image or a still image) from one target moving image.

Step S207: The server control unit 23 transmits the attributeinformation of the target person, the imaging time and the imagingposition of at least one target moving image, the information of thespot corresponding to the imaging position, and the image of the targetperson to the client 40.

As described above, in the information processing system 1 according tothis embodiment, each of a plurality of vehicles 10 generates a movingimage in which a person outside the vehicle is captured and transmitsthe moving image and an imaging time and an imaging position of themoving image to the server 20. The server 20 specifies two or moretarget moving images in which the same person (a target person) appearsout of a plurality of moving images received from the plurality ofvehicles 10. The server 20 detects behavior of the target person fromthe two or more target moving images and estimates attribute informationof the target person based on the detected behavior. Then, the server 20transmits the attribute information of the target person and the imagingtime and the imaging position of the at least one target moving image tothe client 40.

According to this configuration, since behavior of a target person isdetected using two or more target moving images in which the same personappears, detection accuracy for behavior is improved in comparison witha configuration in which behavior of a target person is detected usingone target moving image. Improvement in detection accuracy for behaviorof a target person causes improvement in estimation accuracy forattribute information of the target person. Accordingly, it is possibleto improve convenience of a technique of detecting information on aperson from an image of the onboard camera.

While the disclosure has been described above in conjunction with allthe drawings and embodiments, it should be noted by those skilled in theart that various modifications and corrections can be made based on thepresent disclosure. Accordingly, it should be noted that suchmodifications and corrections are included in the scope of thedisclosure. For example, the functions included in the units or theoperations can be rearranged as long as doing so does not result inlogical inconsistency, and a plurality of units or operations may becombined into one unit or an operation or may be divided.

For example, in the above-mentioned embodiment, attribute information ofa target person is estimated using a learned model with behavior of aperson as input data and with attribute information of the person asoutput data. However, the learned model which is used to estimateattribute information of a target person is not limited to the example.For example, attribute information of a target person may be estimatedusing a learned model with two or more target moving images as inputdata and with attribute information of the target person as output data.

For example, a general-purpose information processing device such as asmartphone or a computer may be made to serve as the constituent unitsof the vehicle 10 or the server 20 according to the above-mentionedembodiment. Specifically, a program in which processing details forrealizing the functions of the vehicle 10 or the server 20 according tothe embodiment are described is stored in a memory of an informationprocessing device and a processor of the information processing deviceis made to read and execute the program. Accordingly, the disclosure canalso be embodied as a program which can be executed by a processor.

What is claimed is:
 1. An information processing system comprising: aplurality of vehicles; and a server configured to communicate with theplurality of vehicles, wherein each of the plurality of vehicles isconfigured to perform: generating a moving image in which a personoutside the vehicle appears; and transmitting the moving image and animaging time and an imaging position of the moving image to the server,and wherein the server is configured to perform: specifying two or moretarget moving images in which the same person appears out of a pluralityof moving images received from the plurality of vehicles; detectingbehavior of the person from the two or more target moving images;estimating attribute information of the person based on the detectedbehavior; transmitting the estimated attribute information of the personand the imaging time and the imaging position of at least one targetmoving image of the two or more target moving images to a client;acquiring information of a spot corresponding to the imaging position ofthe at least one target moving image; and transmitting the acquiredinformation to the client.
 2. The information processing systemaccording to claim 1, wherein the attribute information includesinformation indicating a result of determination of whether a wanderingperson or a suspicious person is applicable.
 3. The informationprocessing system according to claim 1, wherein the server is furtherconfigured to perform: cutting out a moving image or a still image ofthe person from the two or more target moving images; and transmittingthe cut-out moving image or still image to the client.
 4. Theinformation processing system according to claim 1, wherein the serveris further configured to perform: specifying two or more target movingimages in which the same person appears out of a plurality of movingimages of which the imaging times and the imaging positions match withina predetermined range among the plurality of moving images received fromthe plurality of vehicles.
 5. A non-transitory storage medium storinginstructions that are executable by one or more processors and thatcause the one or more processors to perform communication processingwith a plurality of vehicles, comprising: receiving a plurality ofmoving images, which is generated by the plurality of vehicles and inwhich a person outside the vehicle appears, and imaging times andimaging positions of the plurality of moving images; specifying two ormore target moving images in which the same person appears out of theplurality of moving images received from the plurality of vehicles;detecting behavior of the person from the two or more target movingimages; estimating attribute information of the person based on thedetected behavior; transmitting the estimated attribute information ofthe person and the imaging time and the imaging position of at least onetarget moving image of the two or more target moving images to a client;acquiring information of a spot corresponding to the imaging position ofthe at least one target moving image; and transmitting the acquiredinformation to the client.
 6. The non-transitory storage mediumaccording to claim 5, wherein the attribute information includesinformation indicating a result of determination of whether a wanderingperson or a suspicious person is applicable.
 7. The non-transitorystorage medium according to claim 5, wherein the one or more processorsis further configured to execute communication processing comprising:cutting out a moving image or a still image of the person from the twoor more target moving images; and transmitting the cut-out moving imageor still image to the client.
 8. The non-transitory storage mediumaccording to claim 5, wherein the one or more processors is furtherconfigured to execute communication processing comprising: specifyingtwo or more target moving images in which the same person appears out ofa plurality of moving images of which the imaging times and the imagingpositions match within a predetermined range among the plurality ofmoving images received from the plurality of vehicles.
 9. An informationprocessing method which is performed by an information processing systemincluding a plurality of vehicles and a server that is configured tocommunicate with the plurality of vehicles, the information processingmethod comprising: generating, by each of the plurality of vehicles, amoving image in which a person outside the vehicle appears;transmitting, by each of the plurality of vehicles, the moving image andan imaging time and an imaging position of the moving image to theserver; specifying, by the server, two or more target moving images inwhich the same person appears out of a plurality of moving imagesreceived from the plurality of vehicles; detecting, by the server,behavior of the person from the two or more target moving images;estimating, by the server, attribute information of the person based onthe detected behavior; transmitting, by the server, the estimatedattribute information of the person and the imaging time and the imagingposition of at least one target moving image of the two or more targetmoving images to a client; acquiring information of a spot correspondingto the imaging position of the at least one target moving image; andtransmitting the acquired information to the client.
 10. The informationprocessing method according to claim 9, wherein the attributeinformation includes information indicating a result of determination ofwhether a wandering person or a suspicious person is applicable.
 11. Theinformation processing method according to claim 9, further comprising:cutting out, by the server, a moving image or a still image of theperson from the two or more target moving images; and transmitting, bythe server, the cut-out moving image or still image to the client. 12.The information processing method according to claim 9, furthercomprising: specifying two or more target moving images in which thesame person appears out of a plurality of moving images of which theimaging times and the imaging positions match within a predeterminedrange among the plurality of moving images received from the pluralityof vehicles.
 13. The information processing system according to claim 1,wherein the server transmits the estimated attribute information inresponse to a request from the client.
 14. The non-transitory storagemedium according to claim 5, wherein the one or more processorstransmits the estimated attribute information in response to a requestfrom the client.
 15. The information processing method according toclaim 9, wherein the transmitting of the estimated attribute informationis performed in response to a request from the client.